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http://dx.doi.org/10.6109/jkiice.2019.23.4.381

Classification Model of Facial Acne Using Deep Learning  

Jung, Cheeoh (Department of Computer Engineering, Paichai University)
Yeo, Ilyeon (Department of Computer Engineering, Paichai University)
Jung, Hoekyung (Department of Computer Engineering, Paichai University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.4, 2019 , pp. 381-387 More about this Journal
Abstract
The limitations of applying a variety of artificial intelligence to the medical community are, first, subjective views, extensive interpreters and physical fatigue in interpreting the image of an interpreter's illness. And there are questions about how long it takes to collect annotated data sets for each illness and whether to get sufficient training data without compromising the performance of the developed deep learning algorithm. In this paper, when collecting basic images based on acne data sets, the selection criteria and collection procedures are described, and a model is proposed to classify data into small loss rates (5.46%) and high accuracy (96.26%) in the sequential structure. The performance of the proposed model is compared and verified through a comparative experiment with the model provided by Keras. Similar phenomena are expected to be applied to the field of medical and skin care by applying them to the acne classification model proposed in this paper in the future.
Keywords
Deep Learning; Classification; Correlation Analysis; ACNE; CNN;
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